Bayesian generalised ensemble Markov chain Monte Carlo
Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics
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Frellsen, J., Winther, O., Ghahramani, Z., & Ferkinghoff-Borg, J. (2016). Bayesian generalised ensemble Markov chain Monte Carlo. Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics https://www.repository.cam.ac.uk/handle/1810/254509
Bayesian generalised ensemble (BayesGE) is a new method that addresses two major drawbacks of standard Markov chain Monte Carlo algorithms for inference in high-dimensional probability models: inapplicability to estimate the partition function, and poor mixing properties. BayesGE uses a Bayesian approach to iteratively update the belief about the density of states (distribution of the log likelihood under the prior) for the model, with the dual purpose of enhancing the sampling efficiency and make the estimation of the partition function tractable. We benchmark BayesGE on Ising and Potts systems and show that it compares favourably to existing state-of-the-art methods.
JF acknowledge funding from the Danish Council for Independent Research | Natural Sciences. ZG acknowledge funding from EPSRC EP/I036575/1 and Google.
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